CRII: SHF: RUI: Custom Hardware Accelerators for Privacy-Preserving Image Processing

同态加密是一种重要的密码技术，它允许在不解密的情况下直接对加密数据进行计算。计算结果是完全加密的，只有数据所有者可以使用私钥解密。因此，云服务提供商等数据分析师无法查看数据中的任何私人或敏感信息。同态加密的潜在应用包括金融和医疗数据分析和隐私保护机器学习，同态加密还可以用于更广泛的应用，如基因组学、国家安全/关键基础设施和选举。一些新兴的应用使得同态加密变得越来越重要。然而，同态加密存在处理速度慢的问题，这使得它不适用于许多关键应用。此外，它只支持加密数据的加法和/或乘法，这限制了它的应用范围。该项目将使用定制硬件加速器和一种使用加法和乘法近似几种算术和逻辑运算的数值方法来解决这些问题。这个项目将拓宽基于同态加密的实际应用的范围，并为学生提供在数字系统设计、软件编程和密码学等多个领域获得经验的机会。本项目将专注于基于同态加密的图像处理应用。在典型的基于同态加密的系统中，用户对本地设备上的私有数据进行加密并将其发送到云服务器，在云服务器上执行基于同态加密的应用程序。然而，在原始图像中包含噪声或亮度失真可能会对应用程序的结果产生负面影响。因此，该项目的目标之一是开发定制的硬件加速器，用于基于同态加密的噪声抵消和使用现场可编程门阵列的对比度增强。它们需要用于加密数据的除法和最小/最大函数，并且这些运算正在数字上实现。当数据分析师将同态加密驱动的分析结果传送给用户时，会出现另一个问题。它们只能引用与图像的一部分相关联的特定数据点，而不能引用原始图像本身，因为原始图像是加密的。这对用户来说可能是有问题的，因为他们可能不知道数据分析师指的是图像的哪一部分。因此，另一个目标是开发一种用于高亮显示感兴趣区域的图像阈值技术的定制硬件加速器。它需要对加密数据进行比较操作，而数值方法再次被使用。与软件实现相比，该项目旨在将计算运行时开销减少一个数量级至两个数量级。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Homomorphic encryption is an important cryptographic technique that allows direct computation on encrypted data without decryption. The result of the computation is entirely encrypted, and only the data owner can decrypt it using a private key. As a result, data analysts such as cloud-service providers cannot view any private or sensitive information from the data. Promising applications of homomorphic encryption include financial and medical data analytics and privacy-preserving machine learning, and homomorphic encryption can be also used for more diverse applications such as genomics, national security/critical infrastructures, and elections. Several emerging applications makes homomorphic encryption increasingly essential. However, homomorphic encryption suffers from slow processing speed, which renders it impractical for many critical applications. In addition, it only supports addition and/or multiplication for encrypted data, which limits the scope of its applications. This project will address these issues using custom hardware accelerators and a numerical approach that approximates several arithmetic and logical operations using addition and multiplication. This project will broaden the scope of practical homomorphic-encryption-based applications and provide opportunities for students to gain experience in a variety of areas such as digital system design, software programming, and cryptography.This project will focus on homomorphic-encryption-based image-processing applications. In typical homomorphic encryption-based systems, a user encrypts private data on a local device and sends them to a cloud server where a homomorphic encryption-based application is performed. However, the inclusion of noise or brightness distortion in the original images can negatively affect the results of the application. Therefore, one of the goals of this project is to develop custom hardware accelerators for homomorphic-encryption-based noise cancelling and contrast enhancement using field-programmable gate arrays. They require division and min/max functions for encrypted data, and these operations are being numerically implemented. Another problem arises when data analysts convey homomorphic-encryption-driven analysis results to a user. They can only refer to specific data points associated with a section of the image, but not the original image itself as it is encrypted. This can be problematic on the user's side as they may not understand which part of the image the data analyst is referring to. Thus, another goal is to develop a custom hardware accelerator for an image-thresholding technique for highlighting regions of interest. It requires comparison operations for encrypted data, and the numerical approach is being used again. This project aims at reducing the computational runtime overhead by one order to two orders of magnitude compared to software implementations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.